CSIRO PUBLISHING
www.publish.csiro.au/journals/wr
Wildlife Research, 2008, 35, 617–624
Evaluating exotic predator control programs using non-invasive genetic tagging Maxine P. Piggott A,E, Rebecca Wilson B, Sam C. Banks C, Clive A. Marks D, Frank Gigliotti B and Andrea C. Taylor A A
Australian Centre for Biodiversity, School of Biological Sciences, Monash University, Clayton, Victoria 3800, Australia. B Vertebrate Pest Research Unit, Department of Primary Industries (Victoria), PO Box 49, Frankston, Victoria 3199, Australia. C The Fenner School of Environment and Society, The Australian National University, Canberra, ACT 0200, Australia. D Nocturnal Wildlife Research Pty Ltd, PO Box 2126, Wattletree Road Post Office, East Malvern, Victoria 3145, Australia. E Corresponding author. Email:
[email protected]
Abstract. Carnivorous predators are difficult to detect using conventional survey methods, especially at low levels of abundance. The introduced red fox (Vulpes vulpes) in Australia is monitored to determine the effectiveness of control programs, but assessing population parameters such as abundance and recruitment is difficult. We carried out a feasibility study to determine the effectiveness of using faecal DNA analysis methods to identify individual foxes and to assess abundance before and after lethal control. Fox faeces were collected in two sampling periods over four separate transects, and genotyped at five microsatellite loci. Two transects were subject to lethal control between collection periods. DNA was extracted from 170 fox faeces and, in total, 54 unique genotypes were identified. Fifteen biopsy genotypes from 30 foxes killed during lethal control were detected among the faecal genotypes. Overall, a similar number of genotypes were detected in both sampling periods. The number of individuals sampled in both periods was low (n = 6) and new individuals (n = 24) were detected in the second collection period. We were also able to detect animals that avoided lethal control, and movement of individuals between transects. The ability to identify individual foxes using these DNA techniques highlighted the shortcomings of the sample design, in particular the spatial scale and distances between transects. This study shows that noninvasive DNA sampling can provide valuable insight into pre and post fox abundance in relation to lethal control, individual behaviour and movement, as well as sample design. The information gained from this study will contribute to the design of future studies and, ultimately, control strategies.
Introduction Huge efforts and expense can be invested to eradicate or suppress exotic pest populations. However, determining the success of control programs can be difficult, especially if the animals are at low abundance (Morrison et al. 2007). Since its establishment in Australia in the 1870s (Rolls 1969), the European red fox (Vulpes vulpes) has contributed to the decline and extinction of several Australian native species (Dickman 1996). Vulpes vulpes is subject to widespread control in Australia to manage its impacts upon vulnerable native species and agriculture (Saunders and McLeod 2007). However, monitoring the outcome of such programs is difficult using current survey methods. Here we evaluate the utility of individual identification via genetic analysis of faecal DNA to monitor the effectiveness of control strategies on V. vulpes populations. Abundance estimation and monitoring is particularly problematic for mammalian carnivores, which can be sparsely CSIRO 2008
distributed and difficult to trap (Witmer 2005; Gompper et al. 2006). For V. vulpes, current population monitoring methods include counts of animal tracks (e.g. Mahon et al. 1998), spotlight counts (Buckland et al. 1993), rates of faecal deposition (e.g. Sharp et al. 2001), faecal counts, and rate of removal of baits (e.g. Thompson and Fleming 1994; Dexter and Meek 1998). These methods provide only relative indices of activity and are unable to distinguish and count individuals. Genetic analysis of non-invasively collected DNA can distinguish individuals and therefore provide detailed information on exotic pest populations subject to control programs. Such methods have been successfully applied to the management of rare species (Piggott and Taylor 2003). Species identification from remotely collected field samples has been successful on a range of carnivore species, often to distinguish between multiple candidate species (e.g. Gompper et al. 2006). Microsatellite profiling of non-invasive samples can distinguish individuals and has been used effectively on a range of 10.1071/WR08040
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mammalian carnivores (Lucchini et al. 2002; Banks et al. 2003a; Flagstad et al. 2004). Assuming that the genotypes obtained from non-invasive sources are correct and that the probability of a genotypic match among individuals is negligible (Waits et al. 2001), ‘capture histories’ of genotypes are amenable to mark–recapture analysis of population parameters. Depending on the sampling design, the data may be used to estimate a range of parameters, from the minimum number of individuals present in an area to population size and survival probabilities (e.g. Banks et al. 2003b; Piggott et al. 2006a). The genetic data can also contribute information on relatedness, dispersal patterns and population subdivision (Flagstad et al. 2004; Piggott et al. 2006b). This study describes the effectiveness of using faecal DNA analysis for individual identification of wild foxes as part of a larger study comparing the success of five different fox survey techniques (C.A. Marks, unpubl. data). Non-invasive genetic techniques for individual identification have been thoroughly optimised for faeces from captive V. vulpes (Piggott and Taylor 2003; Piggott 2004). Thus, the current study aimed to determine how well these optimised individual identification methods work on faeces collected from wild populations, where the diet and health of individuals cannot be controlled. The goals of this study were to (1) evaluate the success and utility of individual identification from DNA recovered from wild fox faeces; (2) evaluate the utility of using individual identification to monitor population trends associated with lethal control; and (3) provide guidelines for future studies that wish to use noninvasive sampling methods to monitor an introduced predator such as the fox. Materials and methods Study site and design The study was carried out at Melbourne Water Corporation’s Western Treatment Plant at Werribee, Melbourne (37510 S, 144380 E) between October and November 2002. The 10 851 ha study site consists of flat, homogeneous habitat with a large population of foxes not previously exposed to control methods. The study area is used for land and grass filtration of sewage and grazing by sheep and cattle, and consists of a mosaic of flat grassland paddocks and intermittent stands of Cupressus spp. wind breaks with an undergrowth of shrubs or Eucalyptus spp. along drains and roads intersecting the area. Four 5 km transects (T1, TL2, T3 and TL4) were established along a dirt road. Transects were separated by 6 to 7.5 km of road and by straight-line distances of at least 3.5 km. Based on available home range studies of foxes in a similar semi-urban field site in the outer eastern suburbs of Melbourne (White et al. 2006; mean fox home range of 44.6 ha) and central Victorian farmland (Coman et al. 1991; 0.6–1.3 km2 in a semi urban environment and 5–7 km2 in a pasture/woodland environment), this was believed to provide sufficient independence of the four sites. Foxes are thought to use roads for movement and foraging (May and Norton 1996) and are likely to defecate along roads and this sampling strategy has been used for other non-invasive carnivore studies (Adams et al. 2007). Two transects (TL2 and TL4) were randomly allocated to a population manipulation treatment in which M-44 cyanide ejectors (Marks et al. 2003) were used for lethal collection of
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foxes (Treatment). Transects T1 and T3 were used as experimental control sites where no population manipulation was conducted between faecal sampling occasions (Nontreatment). Sample collection The day before each collection period (C1 and C2) all faeces were removed from each transect to ensure only fresh faeces would be collected (Piggott 2004). The first faecal collection period (C1) extended for 4 days from 28–31 October 2002 and was followed by 2 days during which the lethal control method (LC1) was implemented at transects TL2 and TL4. The second sampling period (C2) extended for 4 days from 8–11 November 2002. The lethal control method (LC2) was then implemented for 2 days at all four transects. Putative fox faeces were collected by experienced personnel able to distinguish fox faeces from other species, during daily walks along each transect on each day of C1 and C2. Each faecal sample was placed in a separate brown paper envelope and labelled with the date and a unique identification number and stored dry before DNA extraction (Piggott and Taylor 2003). Individual identification DNA was extracted from fox faeces between 1 and 3 months after collection, using the surface wash extraction method (a) of Piggott and Taylor (2003). Three tetranucleotide (C2088 and C2054; Francisco et al. 1996; and C642; GenBank accession numbers L29310 and L29318) and two dinucleotide canine microsatellite markers (AHT 130; Holmes et al. 1995; and C442:2; Ostrander et al. 1995) were amplified singly in polymerase chain reactions (PCRs) containing 4 mL of DNA extract, 75 mM TRIS-HCl(L) (pH 8.8), 20 mM (NH4)2SO4, 0.01% Tween 20, 2.0 mM MgCl2, 200 mM dGTP, dTTP and dCTP, 20 mM dATP, 0.5% BSA (w/v) (MBI), 5 pmoles of each primer and 0.5 units of Taq polymerase (MBI) in a total volume of 10 mL. Forward primers were labelled with either IRD700 or IRD800 dyes. Cycling was carried out in a MJ Research PTC-100 thermal cycler (Ramsey, MN, USA) starting with 94C for 5 min followed by five touch down cycles of 94C for 30 s, 62–55C for 30 s and 72C for 45 s, followed by 40 cycles of 94C for 30 s, 55C for 30 s and 72C for 45 s with a final step of 72C for 5 min. Products were run through a 6% acrylamide gel and visualised on a Li-Cor Global Edition IR2 DNA Analyser (Lincoln, NE, USA). Comparison and matching of genotypes for individual identification was carried out using the Multilocus Matches Parameters tab in GENALEX version 6.4 (Peakall and Smouse 2006) as well as by eye. In particular, single genotypes with no other matches, as well as genotypes with one or two locus mismatches, were scrutinised thoroughly to ensure non-matches or mismatches to other genotypes were not due to genotyping errors. The probability of a genotypic match (PID) among unrelated individuals and full siblings Waits et al. (2001) was estimated using GENALEX version 6.4 (Peakall and Smouse 2006) for 3, 4 and 5 multilocus genotypes, using genotype data from 30 tissue samples taken from fox carcasses recovered following lethal control at the study site (see ‘Results’).
Genetic tagging of an exotic predator
Lethal control and fox abundance For the first lethal control period (LC1) between the two collection periods, 16 bait stations containing a single M-44 ejector set using the methods described by Marks and Gigliotti (1996) and Marks et al. (2003) were established at 300 m intervals along the Treatment transects TL2 and TL4. The remaining two transects (T1 and T3) were Non-treatment sites, but were prepared and monitored in the same manner as the Treatment sites. Free-feeding was carried out before and during each faecal collection period by placing dried baits on a free-feeding spike (Busana et al. 1998), and burying the top of the device to a depth of 7–10 cm. After the first faecal collection period, the M-44 devices at TL2 and TL4 were loaded with cyanide capsules for two days (LC1), while free-feeding continued at T1 and T3. For the final lethal control period (LC2) the methods described above for LC1 were applied to all four transects. The number of foxes killed following application of lethal control in LC1 and LC2 was determined via the number of fox carcasses recovered on the four transects each morning. A biopsy of ear tissue from each recovered animal was placed in 70% ethanol and DNA later extracted using the ‘salting out’ method of Sunnucks and Hales (1996). Genotyping of biopsy DNA used the same five microsatellites described earlier except only 1 mL DNA was used for PCRs. Genotypes of faecal samples were compared against carcass genotypes to identify dead foxes from which faeces had been collected. This was used to determine whether known individuals from biopsy samples were detected or undetected via faecal genotyping and on which transect detected foxes were located before each lethal control period. As a demonstration of the application of these data to mark– recapture analysis, we used a multi-strata model with live and dead encounters implemented in Mark version 4.3 (White and Burnham 1999) to estimate whether lethal control influenced the survival probability of foxes. Transects were represented as strata with equal movement probabilities, capture probabilities and reporting rates for dead individuals. We tested whether a model incorporating an effect of lethal control on fox survival better explained the data than a model with no effect of lethal control. Assessment of reliability Our previous pilot studies suggested that three PCR replicates were sufficient to minimise genotyping error rates from captive fox faeces (Piggott and Taylor 2003; Piggott 2004). These two previous studies using the same surface wash method used in this study had very low genotyping error rates. We carried out a further pilot study performing six replicates per sample per locus for each of 10 randomly selected samples from wild foxes at the study site. Based on error rates estimated from genotyping the 10 samples in this manner, the approach described in Piggott et al. (2006a) demonstrated that reliable genotypes could be obtained from three replicate PCRs. Briefly, genotyping error rates were calculated from the six replicate PCRs separately for allelic dropout and false alleles and averaged across loci. A genotype was accepted if it matched in at least three of the six PCR replicates. We then used the GEMINI program, version 1.2.0 (Valière et al. 2002), to simulate genotyping to estimate the
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ideal number of replicate PCRs required for accurate genotypes. However, to ensure the correct genotype was identified in this study, we carried out a minimum of four replicate PCRs per locus, per sample. A genotype was accepted if it matched in at least three of the four PCR replicates. If a genotype did not match in at least three of the replicates, a further two PCR replicates were carried out to a total of six PCR replicates. We determined the genotyping error rate retrospectively for all faecal samples that could be genotyped. Results Sample collection From C1 and C2, 112 of 170 (65.8%) putative fox faeces yielded 3, 4 or 5 locus genotypes (Table 1). In total, 39 scats (22.9%) failed to amplify for any locus and 19 (11.2%) could only be genotyped at one or two loci. There was variation between days in the proportion of samples that could be successfully genotyped (Table 2, Fig. 1). C1 samples had a higher genotyping success rate than C2, but more faeces were collected in C2 (Table 2). Individual identification Based on 5-locus genotypes from 30 fox carcasses, the probability of a genotypic match for unrelated individuals and full siblings was ~2.3 in 10 000 and 2.4 in 100 respectively (Table 1). In total, 54 distinct faecal genotypes were identified from the 112 genotyped samples (Table 1). For both periods, the detection rate of new genotypes had not begun to asymptote on the final day of sampling (Fig. 1). The number of faeces collected per genotype ranged from 1 to 13 (mean 2.07, s.d. 2.34), with 63% of genotypes being detected in only one faecal sample (see Appendix 1). Genotypes detected only once were sampled on all four transects. Five individuals from transects T1 and TL2 were identified from between 5 and 13 faeces each and dominated the number of samples that could be reliably genotyped (42/112: Appendix 1).
Table 1. The probability of a genotypic match (PID) for unrelated individuals and full siblings and the number of unique genotypes at 3, 4 and 5 loci for 30 Vulpes vulpes biopsy genotypes, and the number of unique genotypes and genotyped faecal samples at 3, 4 and 5 loci for 54 faecal genotypes 5-locus genotypes Biopsy samples from carcasses 2.27 104 PID: Unrelated PID: Full siblings 2.36 102 No. of unique biopsy 29 genotypes Faecal genotypes from faeces No. of unique faecal 41 genotypes No. of genotyped faeces 97 (86.6) (% total genotyped)
4-locus genotypes
3-locus genotypes
1.17 103 5.02 102 1
4.93 103 9.55 102 0
9
4
11 (9.8)
4 (3.6)
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Table 2. The number of Vulpes vulpes faecal samples collected and successfully amplified and the number of distinct genotypes detected in each collection period (C1 and C2) and the number of foxes killed and detected in each control period (LC1 and LC2) Transect
C1 No. of No. of faeces distinct collected genotypes (amp.%)
LC1 No. of No. No. foxes detected undetected killed in C1 in C1
No. of faeces collected (amp.%)
C2 No. of No. No. distinct re-sampled new genotypes from C1 genotypes
LC2 No. of No. foxes previously killed detected
T1 TL2 T3 TL4
19 (89.5) 21 (71.4) 8 (75.0) 19 (63.2)
10 7 5 9
0 3 0 8
0 2 0 4
0 1 0 4
49 (71.4) 27 (48.2) 3 (66.7) 24 (66.7)
13 5 2 10
4A 0 0 2
9 5 2 8
8 1 3 7
2 0 2 5
Total
67 (73.1)
31
11
6
5
103 (61.2)
29
6
24
19
9
35
C1 C2 C1 and C2
This includes a recapture of an animal sampled in C1 at TL4.
No. of genotypes detected
45 40 35
C1 genotyped faeces C1 no. of genotypes C2 genotyped faeces C2 no. of genotypes
No. of genotypes
A
30 25 20 15
30 25 20 15 10
*
5
10
0
5
T1
TL2
T3
TL4
Overall
Transect
0 1
2
3
4
Day Fig. 1. The number of Vulpes vulpes faeces genotyped per day and the cumulative number of genotypes for Collection 1 (C1) and 2 (C2).
Fig. 2. The number of unique genotypes detected for each collection period (C1 and C2), and the number of genotypes sampled in both collection periods in the two treatment transects (TL2 and TL4) and the non-treatment transects (T1 and T3) and overall. (* One of the resampled genotypes in T1 was sampled in TL4 in C1).
Lethal control and fox abundance The number of distinct genotypes detected in C2 (29) was similar to C1 (31) (Fig. 2, Table 2). Although fewer genotypes were detected in TL2 after LC1 there was a slight increase in the number of genotypes detected in treatment transect TL4 after LC1 (Fig. 2, Table 2). New genotypes (24) were detected in all four transects in C2, with the largest number in TL4 (10), which also had more foxes killed in LC1 compared with the other treatment transect TL2 (Table 2). Genotypes from 15 of the 30 fox carcasses recovered were detected in faeces in C1 and C2 (Table 2). Genotypes matching five of the six foxes killed and recovered after LC1 were detected previously at the same transect, but not during the subsequent collection. Faeces from the remaining individual (CO2086/W49) were detected on T3 but the carcass was recovered on TL4. Genotypes of six animals were detected in faeces collected during both C1 and C2 (Table 2). Three of these foxes survived LC1 on transects TL4, along with a fourth individual (W9) detected in TL4 during C1 but not C2 and then killed in LC2 in the same transect. Two individuals were detected moving between transects, W19 from a treatment transect to a non-treatment transect (TL4 to T1) and vice versa for W49 (T3 to TL4). The multi-state mark–recapture model incorporating an effect of lethal control on fox survival had an evidence ratio 1.28 over a
model with constant survival probability (DAICC of null model = 0.49). The survival probability of foxes subject to lethal control was ~36% (CI = 30 to 56%) of those not subject to the baiting program. Assessment of reliability From a total of 2580 successful PCRs we retrospectively calculated a genotyping error rate of 0.81% (21/2580) with similar rates of false alleles (9, or 0.35%) and allelic dropout (12, or 0.47%). In 82.5% of cases, a sample that amplified had a matching genotype in all four PCR replicates. Overall, 17 pairs of individuals in this study differed at one locus and were thoroughly scrutinised before acceptance as separate genotypes. Support for the reliability of genotypes obtained from faecal DNA is also provided by 15 matches between faecal genotypes and fox carcasses recovered from the lethal control treatments. Discussion This study has shown that genotyping DNA obtained from faeces is an effective method for identifying individual red foxes and has great potential for assessing pre and post fox abundance in relation to lethal control, as well as individual
Genetic tagging of an exotic predator
behaviour such as bait avoidance and fox movement. We identified numerous foxes sampled on the transects among those killed by the M-44 cyanide ejectors. Nevertheless, the number of animals in the treatment and non-treatment transects was broadly similar pre- and post-treatment. Some individuals avoided lethal control and new animals were detected on all transects following treatment. Either these individuals were present but not detected during the first collection period (C1) or were animals from the surrounding area that moved into the study area following lethal control. We did not find much support for an effect of lethal control on fox survival in this feasibility study. Our findings suggest that the sample design, in particular the interspersion of the treatment and control sites was insufficient to reduce fox abundance. The detected movement of two animals between transects also highlights the shortcomings of the sample design and our assumption that transects separated by 3.5 km were far enough apart to be independent from one another. The ability to identify and track individual foxes via non-invasive genetic methods has highlighted the limitations of the study design, which may not have been apparent based on assessment using conventional methods. Although there were some obvious limitations in this pilot study, individual identification of foxes does have great potential for estimating parameters such as abundance, survival, movement and immigration. Accurate estimation of these parameters via mark–recapture analysis is likely to require more intensive sampling than conducted in this study. Other methods can also monitor relative abundance trends (e.g. spotlight counts: Buckland et al. 1993; and counts from animal tracks: Mahon et al. 1998), but non-invasive DNA sampling is the only method that can identify and track individuals. Individual identification may also prove useful for targeting or monitoring individual foxes suspected of recurrently preying on susceptible populations (e.g. little penguins; Norman 1971; Dann 1992) as well as animals that may consistently avoid lethal control. No conventional detection or monitoring method has the ability to discern the contribution of specific individuals to predation events. We showed that at least four individuals avoided the first lethal control treatment and two of these were not recovered in the second application of lethal control. Genotypes from non-invasive samples can also be used in genetic analyses that detect recruitment into local populations, particularly for long-term studies on the effects of lethal control on populations. For example, the genetic data could be used to determine whether the level of recruitment differs between populations/transects subject to lethal control v. the level of recruitment in normal populations. In this study, 24 new animals were detected in the second collection period and it would be interesting to determine if these animals were from the surrounding area and moved into the study area following lethal control and after removal of some of the local fox population. Genetic assignment tests can be used to determine whether recruits are local individuals or recruits from elsewhere. Peakall and Lindenmayer (2006) showed that following removal of bush rats (Rattus fuscipes) from local populations, local extinction occurred at one site, but for all other sites the population size increased and that population
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recovery was primarily driven by residual animals rather than immigrants. Compared with an earlier pilot study on microsatellite genotyping from captive fox faeces, this study on wild faeces experienced a lower overall genotyping success (69% v. 100% in Piggott and Taylor 2003). In addition, there was variation in amplification success between days (range: 48% to 89%). Our amplification success compares well with other non-invasive studies on carnivores, e.g. red wolves: 50% (Adams and Waits 2007) and 27.9% (Adams et al. 2007), mountain lions: >75% (Ernest et al. 2000), kit foxes: 79% (Smith et al. 2006) and coyotes: 61% (Kohn et al. 1999). It is unlikely that the samples in this study that failed to amplify successfully were from another species (whose DNA may not amplify with the primers used here) as experienced personnel were used to identify and collect faeces. However, if required, species identification of V. vulpes faeces can be confirmed using the molecular approach of Berry et al. (2007). Possible reasons for the discrepancy in genotyping success include differences in health and diet of captive and wild foxes, which is likely to vary greatly among wild foxes. Studies have also shown that some individuals produce ‘better’ faecal DNA than others (Goossens et al. 2000; Piggott and Taylor 2003). In addition, differences in genotyping success between collection days may be due to different weather conditions, such as rain or heavy dew overnight. This suggests some foxes may not provide faeces with amplifiable DNA, from which genotypes cannot be obtained, which means they could not be detected in this study. The fact that 15 of the foxes killed during the lethal control periods were not detected from scats collected in the sampling days prior even though it is highly likely they were on the transects also supports the idea that at least some individuals did not provide faeces with amplifiable DNA or their faeces were not detected during collection in this study. Although it is not unusual in non-invasive genetic studies to be unable to obtain genotypes from all collected samples, this does mean that not all animals in a population may be detectable using these methods, which may impact upon estimating population parameters such as abundance, recruitment and density, as well as survivorship following control strategies. However, the ability to detect and track a large proportion of the animals present can still provide important information on population parameters and trends. The more frequent detection of some genotypes on the transects may suggest the heterogeneous use of transects by individuals. The use of scats to mark territory and food resources by the red fox has been documented (Macdonald 1980) and the majority of scats located on a transect during our study were found within 10 m of the bait station. It is therefore possible that scat location is not independent of bait stations and scat frequency may be biased towards foxes that consume baits and mark the locations. One study of fox baiting practices on transects used semi-permanent marker dyes in baits, and cyanide ejectors to show that some foxes in a population had consumed significantly more baits than others and some had consumed none at all (Marks et al. 2003). However, it remains unknown if this is due to dietary preference or more frequent patrolling of transects by some individuals.
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Although only five microsatellite loci were used in this study, we were confident in our ability to identify and distinguish individuals, and, most importantly, full siblings, based on the 5-locus genotypes. However, the probability of a genotypic match (PID) for full siblings for 4- and 3-locus genotypes were lower than is ideal for non-invasive studies (
